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A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19
Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10096268/ https://www.ncbi.nlm.nih.gov/pubmed/37043469 http://dx.doi.org/10.1371/journal.pone.0284370 |
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author | Rainey, Andrew L. Liang, Song Bisesi, Joseph H. Sabo-Attwood, Tara Maurelli, Anthony T. |
author_facet | Rainey, Andrew L. Liang, Song Bisesi, Joseph H. Sabo-Attwood, Tara Maurelli, Anthony T. |
author_sort | Rainey, Andrew L. |
collection | PubMed |
description | Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and interpretation of community infection trends. The Centers for Disease Control and Prevention National Wastewater Surveillance System (CDC NWSS) recommends using the wastewater flow rate or human fecal indicators as population normalization factors. However, there is no consensus on which normalization factor performs best. In this study, we provided the first multistate assessment of the effects of flow rate and human fecal indicators (crAssphage, F+ Coliphage, and PMMoV) on the correlation of SARS-CoV-2 wastewater concentrations and COVID-19 cases using the CDC NWSS dataset of 182 communities across six U.S. states. Flow normalized SARS-CoV-2 wastewater concentrations produced the strongest correlation with COVID-19 cases. The correlation from the three human fecal indicators were significantly lower than flow rate. Additionally, using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) significantly improved correlation values over samples that were analyzed with real-time reverse transcription quantitative polymerase chain reaction (rRT-qPCR). Our assessment shows that utilizing flow normalization with RT-ddPCR generate the strongest correlation between SARS-CoV-2 wastewater concentrations and COVID-19 cases. |
format | Online Article Text |
id | pubmed-10096268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100962682023-04-13 A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 Rainey, Andrew L. Liang, Song Bisesi, Joseph H. Sabo-Attwood, Tara Maurelli, Anthony T. PLoS One Research Article Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and interpretation of community infection trends. The Centers for Disease Control and Prevention National Wastewater Surveillance System (CDC NWSS) recommends using the wastewater flow rate or human fecal indicators as population normalization factors. However, there is no consensus on which normalization factor performs best. In this study, we provided the first multistate assessment of the effects of flow rate and human fecal indicators (crAssphage, F+ Coliphage, and PMMoV) on the correlation of SARS-CoV-2 wastewater concentrations and COVID-19 cases using the CDC NWSS dataset of 182 communities across six U.S. states. Flow normalized SARS-CoV-2 wastewater concentrations produced the strongest correlation with COVID-19 cases. The correlation from the three human fecal indicators were significantly lower than flow rate. Additionally, using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) significantly improved correlation values over samples that were analyzed with real-time reverse transcription quantitative polymerase chain reaction (rRT-qPCR). Our assessment shows that utilizing flow normalization with RT-ddPCR generate the strongest correlation between SARS-CoV-2 wastewater concentrations and COVID-19 cases. Public Library of Science 2023-04-12 /pmc/articles/PMC10096268/ /pubmed/37043469 http://dx.doi.org/10.1371/journal.pone.0284370 Text en © 2023 Rainey et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rainey, Andrew L. Liang, Song Bisesi, Joseph H. Sabo-Attwood, Tara Maurelli, Anthony T. A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 |
title | A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 |
title_full | A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 |
title_fullStr | A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 |
title_full_unstemmed | A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 |
title_short | A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19 |
title_sort | multistate assessment of population normalization factors for wastewater-based epidemiology of covid-19 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10096268/ https://www.ncbi.nlm.nih.gov/pubmed/37043469 http://dx.doi.org/10.1371/journal.pone.0284370 |
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